2010/5/27 arthur de conihout <arthurdeconihout@gmail.com>:
> I try to make me clearer on my project :
> [...]
I think I understood now. Thank you for explanation.
> original = [s / 2.0**15 for s in original]
>
> nframes=filtre.getnframes()
> nchannels=filtre.getnchannels()
> filtre = struct.unpack_from("%dh" % nframes*nchannels,
> filtre.readframes(nframes*nchannels))
> filtre = [s / 2.0**15 for s in filtre]
>
> result = numpy.convolve(original, filtre)
Additionally to what David pointed out, it should also suffice to
normalise only the impulse response channel.
Furthermore, I would spend some thought on what "normalised" actually
means. And I think it can only be understood in Fourier domain. When
taking the conservation of energy into account, i.e. the conservation
of the L2 norm of the impulse response function under Fourier
transformation, it can be normalised in time domain also by setting
the time-domain L2 norm to unity. (This is already much different
from the maximum normalisation.) Then the energy content of the
signal before and after the processing is identical. I.e., it
emphasises some frequencies in favour of others which are diminished
in volume.
A better approach might be in my opinion to use the (already directly)
determined transfer function. This can be normalised by the power of
the input signal, which can e.g. be determined by a reference
measurement without any obstruction in defined distance, I would say.
> result = [ sample * 2.0**15 for sample in result ]
> filtered.writeframes(struct.pack('%dh' % len(result), *result))
It's to me too a mystery why you observe this octave jump you reported
on. I guess it's an octave, because you can compensate by doubling
the sampling rate. Can you check whether your playback program loads
the output file as single or double channel? Also I guess some
relation between your observation of "incomplete convolution" and this
pitch change.
And now, I'm very irritated by the way you handle multiple channels in
the input file. Actually you load maybe a two-channel input file,
extract *all* the data, i.e. data from ch1, ch2, ch1, ch2, ..., and
then convolve this? For single-channel input, this is correct, but
when your input data is two-channel, it explains on the one hand why
your program maybe doesn't work properly and on the other why you have
pitch-halfening (you double the length of each frame). It is I think
possible that you didn't notice more strange phenomenon, since the
impulse response function is applied to each datum individually.
> i had a look to what you sent me i m on my way understanding maybe your
> initialisation tests will allow me to make difference between every wav
> formats?
Exactly! It should be able to decode at least most wavs, with
different samp widhts and different channel number. But I think I
will myself in future hold to David's advice ...
> i want to be able to encode every formats (16bit unsigned, 32bits)what
> precautions do i have to respect in the filtering?do filter and original
> must be the same or?
> Thank you
All problems would go away when you use the transfer function
directly. It should be present as some function, which can easily be
interpolated to the frequency points your FFT of the input signal
yields. Interpolating the time-domain impulse response is not a good
idea, since it assumes already frequency-boundedness, which is not
necessarily fulfilled, I guess. Also it's much more complicated.
When you measure transfer function, how can you reconstruct a unique
impulse response? Do you measure also phases? When you shine in with
white noise, do autocorrelation of the result and Fourier transform,
the phases of the transfer function are lost, you only obtain
amplitudes (squared). Of course it is the same to multiply in Fourier
space with the transfer function as to convolve with its Fourier-back
transform, but I'm not yet convinced that this is a good idea to do it
in time domain tough ... Can you maybe give some hint?
This means you essentially convolve with the autocorrelation function
itself - but this one is symmetric and therefore not causal. I think
it's strange when sound starts before it starts in the input ... the
impulse response must be causal. Anyway, this problem remains also
when you multiply with the plain real numbers in Fourier domain which
result from the Fourier transform of the autocorrelation function -
it's still acausal. The only way to circumvent this I'm seeing
currently is to measure the phases of the transfer function too - i.e.
to do a frequency sweep, white noise autocorrelation is then as far as
I think not sufficient.
Sorry, I feel it didn't came out very clear ...